Exemplary embodiments generally relate to data processing and, more particularly, to machine learning and to database schemes.
Recommenders are widely used in movies, music, products, and even social networking. Recommenders are computers and software that recommend movies, music, books and other items that may appeal to a user. Conventional recommenders use ratings information to extrapolate preferences of users. Users, for example, are commonly asked to rate items using a numeric “star” system (e.g., 1 star=“Hate it” and 5 stars=“Love it”). Conventional recommenders then use latent factor models to recommend movies, music, and other items that may appeal to users. Conventional recommenders, however, fail to account for changing user tastes. A user's preferences may change over time, but conventional recommenders fail to reflect these changes.